In various image processing applications, it may be desirable to track an object, such as a face, between successive frames in a video. In order to track a face from one frame to the next, the translation motion of the face and the scaling of the face from one frame to the next may need to be determined. However, the determination of the translation motion and the scaling of a face from one image to the next may be a computationally intensive process which may be a challenge, at least for those devices with limited computational resources, to perform in an efficient and timely manner.
Face tracking may be computationally intensive for various reasons. For example, some face tracking techniques analyze an entire frame or at least a relatively large portion of the frame, as opposed to focusing upon the face region. Additionally, or alternatively, some face tracking techniques utilize multi-dimensional searches which further add to the computational requirements. As such, it would be desirable to provide an improved technique for face tracking between frames, such as frames of a video, that provides accurate results with reduced computational requirements.